One of my favorite things about SRECon is watching how the industry is marching ever closer to constrained Kalman filters and I’m going to seem like a prophet before long.
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I just nearly launched into a discussion on the Nyquist-Shannon Sampling Theorem but decided to just let that ship stay anchored in harbor.
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But for real, it’s very close to the point where tools and technologies exist to sufficiently model computing as a continuous, linearizable system, and when that happens it’ll be a watershed moment for the monitoring and diagnostics industry.
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Replying to @EmilyGorcenski
Do you mean something like a proof of universal linearizability, or the ability to build such a beast in practice?
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Replying to @jcape
Just the ability to build a sufficiently accurate model
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Replying to @EmilyGorcenski @jcape
Linearizability is not even strictly necessary, and I’m not even convinced that we couldn’t revolutionize the monitoring world right now using unscented Kalman filters
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Replying to @EmilyGorcenski
Gotcha. So this seems (to someone that had never previously heard of Kalman filters) to be a new application of the techniques used by quants to build trading strategies.
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It comes from a similar area, yeah. It’s not exactly the same. A closer analog is embedded control, like flight controllers
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Replying to @EmilyGorcenski
Yep. The seemingly now-defunct
@VerdandeTech was working on that problem some years ago, albeit from an angle they labeled "case-based reasoning".0 replies 0 retweets 0 likesThanks. Twitter will use this to make your timeline better. UndoUndo
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